Merger Effects with Product Complementarity: Evidence from
Colombia’s Telecommunications

Juan Vélez

February 04, 2017

Intro

  • Thanks, it is an honor to be here
  • First time presenting to Colombian audience
  • Work in progress, comments welcome
  • I'll talk about mergers with potential complements, let me start by defining…

Product complementarity

Two ways one can think of complementarity

  • Cross-price elasticity is negative
  • Joint consumption yields extra utility
  • Text book example: coffee and sugar.
    • A decrease in the price of coffee increases demand for sugar
  • The two definitions are really the same.
  • I don't mean complements in the production sense
    • Actually I abstract away from it

Mergers with product complementarity

  • Complements: consumers get extra utility from joint consumption
    • standalone consumption is possible
  • The complementarity creates pro-competitive effects

  • Bundling could create anti-competitive effects

  • Example: coffee and sugar
    • Reduction in the price of coffee increases profits for sugar producer
    • The merger internalizes those extra profits (pro-competitive)
    • Merged firm can bundle and use it for price discrimination (anti-competitive)
  • Different than horizontal and vertical mergers

  • Twofold interest: dual character, regulatory agencies

  • Horizontal: produce substitutes (Staples and Office Depot, Bavaria and Águila)

  • Vertical: components of a final good (Time Warner and Turner Corp, Gabrica and Zoetis)

Recent examples: smartphones and headsets

  • 2014, 3.2 billion dollars (9.6e12)
  • Beats: 2 business (music and electronics)
  • Authorities worried about music and not electronics

Recent examples: phone and TV

  • 2015 67 billion (1.8e13)
  • AT&T second largest telecom

Recent examples: Internet and cable TV

  • AT&T strikes back in 2016
  • 85 billion (Time Warner is cable TV)
  • It hasn't happened yet
  • Trump opposes to it because he i afraid of media and it consolidates media power

Recent examples: Comcel and Telmex

  • Sleaziest merger in history
  • involved larges carrier and second largest ISP
  • Comcel market shares have dwindle down since 2009

Recent examples: ETB and …

  • Still don't know if it will be a merger of complements
  • Some complementarity is bound to happen

  • These kinds of mergers happen often and affect lots of consumers

Motivation

  • Vast literature studying mergers
    • Vertical
    • Horizontal
  • Regulators around the world have handbooks that describe the scrutiny process.
    • Horizontal Merger Guidelines (2010) in the US
    • Ley 1340 de 2009 in Colombia
  • Mergers with complementary goods happen often
    • Telmex-Comcel, Tigo-Une, Telecom-Movistar…
  • Technological convergence (TV used to be independent from Internet)
  • Surge in mergers with complements
  • Technological convergence
  • guidelines define how to scrutinize and remedies to problems caused by allowed mergers
    • Divestiture
    • Forbid bundling
  • Bundling might be actually a good thing if the complementarity is strong

Overview

  • I study mergers of firms producing (potentially) complementary products.
    • Coffee and sugar, smartphones and earbuds, cellphone and Internet.
  • Why should we care?
    • Theory predictions are ambiguous (Choi, 2008; Anderson et al., 2010)
    • Often happen: Apple-Beats, AT&T-DirecTV, AT&T-Time Warner
  • This paper
    • Estimates a discrete choice demand (substitution patterns)
    • Finds that wireless and wired services are complements (caveats)
    • Uses preference estimates to evaluate 2 alternative scenarios
      • Merger of ETB and Avantel
      • De-merger of Claro into Telmex and Comcel
  • I don't know if goods are complements a priori (estimate demand, get )

Relation to literature

  • Theory papers
    • Spector (2007)
    • Peitz (2008)
    • Choi (2008)
    • Anderson, Loertscher, and Schneider (2010)
    • Flores-Fillol and Moner-Colonques (2011)
  • Empirical papers
    • Gentzkow (2007)
    • Ribeiro and Vareda (2010)
  • Spector shows bundling can be used to sustain collusive behavior
  • Choi study effects of mergers on price, investment and R&D
  • Peitz circumstances under which bundling can blockade entry
  • Anderson builds on Choi: when can a merger benefit consumers, harm outsiders and benefit insiders

  • All assume no cost energy

Data and industry

Colombia's telecom sector: the timeline

  • Crash course on history of telecom
  • I changed my perception about the sector after reading more
  • Developed as they normally do.
  • 91 increased competition
  • Claro has been losing market shares
  • VNMOs have increased competition

Colombia's telecom sector: the players

  • Characterized by a few big firms (less than 10)
    • Telephone & Internet: Claro, Movistar, UNE, ETB and Emcali.
    • Mobile: Claro, Movistar, Tigo, Avantel.
    • TV: Claro, DirecTV, ETB, Movistar, UNE.

A fringe of small ones (more than 70).

  • The actual market structure is also similar to those in developed nations. Certainly better than the region. (16 billion revenues)
  • Small ones are exclusively wired
  • VNMO are changing the landscape

Data

I use data from two sources

  • Data provided by the Comisión de Regulación de Comunicaciones (CRC)
    • Firms fill out Form 5 at the end of every quarter.
    • Market shares and bundle characteristics.
    • 762 markets (3,048 market-quarters).
    • 82 firms.
  • I use 2 datasets
  • Observe universe of firms and products
  • Amazing amount of detail on good's characteristics
  • I do drop some small towns

Data: Form 5

  • Decree defines information to be collected
  • Astounding amount of detail
  • I observe standalone and bundles
  • Location up to stratum, price, and many characteristics
  • All these characteristics will help identify complementarities later

Data Household survey

  • Data on households (CPS-like, quarterly)
    • mean schooling
    • household head between 25-45 years old
    • monthly income
    • family size
  • (Households summary statistics)
  • The other data I use is GEIH
  • Sample households corresponding to each market
  • This data will allow me to let households with different observed characteristics to respond differently to bundle's attributes
  • Rich less price elastic, educated diff attitude toward premium channels, Older households may not care about download speed.

Summary statistics: firms

Summary statistics of firm attributes
Means and standard deviations
2015 - Q1 2015 - Q2 2015 - Q3 2015 - Q4
Firm level
  # plans a 22.831 24.12 21.965 22.631
   21.124 22.167 19.765 18.149
  # cities b 24.312 24.111 27.304 27.125
   20.651 21.31 22.651 24.435
  Observations 82 82 81 79
Firm-market level
  Price c 19.203 19.605 21.317 22.481
   14.319 16.129 15.874 15.104
  # plans d 6.819 6.617 6.429 6.597
   6.776 6.443 5.601 6.335
  # subscribers 32341.879 32346.814 32410.612 32496.101
   31516.417 30478.309 32271.051 33064.021
  Observations 2341 2346 2347 2351
a: total plans offered across markets.
b: number of cities where firm operates.
c: price of bundle in 2015 dollars. The number of households subscribed is used as weights.
d: number of plans offered in market.
  • Main takeaway is that the firms are VERY diverse (table isn't much useful)
  • Standard deviations are huge! because of the wide range of many of these variables
  • Average firm 20 plans, with some ISPs having very few (Eje Cafetero 2 plans)
  • Define what a plan is: change in characteristics define a different plan
  • Average firm operates in 24 cities, but mobile operate almost everywhere and lots of small ISPs

Summary statistics: plans

Summary statistics of plan attributes
Means and standard deviations
2015 - Q1 2015 - Q2 2015 - Q3 2015 - Q4
Price a 26.388 29.718 28.31 27.55
21.127 28.271 21.877 22.101
Download speed b 2.285 3.121 3.426 3.375
1.921 2.815 3.107 3.264
Premium channels c 0.312 0.336 0.345 0.388
0.304 0.325 0.318 0.342
HD channels 0.265 0.211 0.293 0.238
0.187 0.177 0.265 0.148
Gb Included (Mobile) d 3.421 3.678 3.762 3.518
2.901 2.875 2.886 3.107
Minutes included (Cell phone) 301.078 409.123 410.356 452.31
100.078 102.891 132.376 137.552
Gb included (Cell phone) e 2.678 2.889 3.173 3.204
4.143 5.168 5.284 4.178
Minutes included (Phone) 435.218 482.337 473.215 461.461
212.166 283.472 251.639 258.813
Observations 28934 28965 28919 28727
a: Price in 2015 dollars.
b: of bundles with Internet. In megabits per second.
c: channels like HBO. For bundles with TV.
d: mobile refers to mobile internet provided through a dongle.
e: access to internet using a cell phone as opposed to using a USB modem and a computer.
  • Average plan is around 26 dollars
  • Download speeds have been increasing but don't match those reported by the Ministerio de Telecomunicaciones
  • A third of cable TV include some premium channels

Summary statistics: correlations

  • Remember sugar and detergent?
  • For my purpose this one is more interesting
  • Blue 'happen together, red 'either is bought'
  • Symmetric of course. Just focus on the top left half
  • suggest that Mobile internet and Cellphone are complements: maybe just available together
  • mobile and phone substitutes? not necessarily (mobile may be used just coz broadband not available)
  • Good first approach though

Demand

Estimating substitution patterns

Following Gentzkow (2007)

  • Suppose two goods A and B.

(1)uA=δAαpA+ξA
(2)uB=δBαpB+ξB
(3)uAB=uA+uB+Γ

  • δ: mean utility
  • ξ: unobservable variation in utility.
  • Γ: utility/dis-utility of joint consumption
  • Sign of Γ determines if complements or substitutes (more details).
  • Gentzkow motivation: are online newspapers substitutes for online?
  • bundles are modeled as just another good available to consumers
  • 2 goods, 4 possible bundles: A, B, AB, outside option
  • Utility 3 components: good specific unobserved, price and mean utility
  • Utility of bundles: sum of utilities + or - something
  • Sign of Gamma determines subst or complts

Empirical demand I

  • ℱ firms indexed by f{1,2,3,...,}

  • g{1,2,3,...,Gf} indexes the standalone goods provided by a firm

  • b{1,2,3,...,(Gf)} indexes bundles provided by a firm

  • j{0,1,2,3,...,2max{G1,G2,...,G}} are types of bundles

  • In the application there are 32 possible bundles j{0,1,...,31}
    • outside bundle is 0
    • singleton bundles are 1-5
  • Example: Sutatausa. ℱ = 4 (Claro, UNE, Coltel and ETB). Claro provides TV, Mobile Internet and Cellphone. Bundles: TV/Mobile (1.2Gb), TV/Mobile (2Gb), Cellphone/TV).

  • I need to adapt to more than 2 goods

Empirical demand II

  • Utility of a single good g

(4)u¯ig=pgα¯i+kxgkβ¯igk+ξg

(5)α¯i=α+rzirαro+νipαu
(6)β¯igk=βgk+rzirβgkro+νigkβgku

  • k are non-price characteristics of the good.
  • r are characteristics of an individual (household).
  • In principle one can run a logit (alpha u and alhp o == 0)
  • Equivalent to say that poor and rich households respond to price
  • Infamous for messing up substitution patterns

Empirical demand III

  • utility of a bundle b of type j

(7)uib={εib,if b=0gbu¯ig+Γj(b)+εib,if b>0

  • εib T1EV
  • Γj(b)=0 for b<5
  • Now we can define the bundle's utility (bundle includes outside! and singleton)
  • So the shocks are realized and consumers choose which bundle
  • Explain why T1EV
  • Get an expression of how likely it is that a consumer will choose j (market share)

Empirical demand IV

  • Integrating over ε

(8)sb=νzexp[gbu¯igf+Γj(b)]1+f=1l=1(Gf)[expglu¯ig+Γl(b)]dFzdFν

  • Follow Berry (1994)
  • Hausmann instruments and BLP instruments
  • GMM
    • E[Zmω(θ)]=0
  • integrating epsilon out, we get an expression of market shares in terms of parameters of interest in particular GAMMA
  • Basic idea? find Gamma that sets estimated shares equal to osberved
  • Why not do it? Parameters enter non linearly. With quantum computers we could.
  • Berry found a way (3steps): 1 pick arbitrary Theta2, do the berry iteration, define omega = delta- linear construct GMM objective

Identification of Γ

Suppose sA, sB and sAB are observed

  • Large sAB relative to sA and sB could indicate they are complements (Γ>0)
    • but also that they are bad substitutes (sugar and detergent)
    • or correlated preferences (cinephiles)
  • To get true complementarity I need exclusion restrictions
    • Suppose variable x enters δA but not δB nor Γ
    • observing x adds three pieces of information: sA(x), sB(x) and sAB(x)
  • Berry has proofs showing parameters are formally identified.
  • I want to add the intuition of why they are identified
  • Large market s_AB are not enough
    • Terrible substitutes (sugar and detergent)
    • Correlated preferences(cinephiles)
  • Key identification: exclusion restrictions (variables included in the mean utility of one of the goods but not on the other's)
  • Observing 2 values of such variables formally identifies: adds 3 moments and just one additional parameter.

Identification of Γ: intuition

Suppose

  • That the goods are Internet (A) and TV (B)

  • Large sAB relative to sA and sB

  • The exclusion restriction is download speed (DS)
    • If Γ>0, DS should increase sA and sB
    • If Γ=0, DS should not alter sB
  • Internet (A) and TV (B) and true Gamma >0. increase in download speeds should also affect S_B

  • If gamma = 0 shouldn't

  • Or think of 2 markets where speed varies. If the market with higher speed has also larger shares for TV and bundle, the model has to rationalize that with a large Gamma

Endogeneity and instruments

Unobserved characteristics may be correlated with price

  • Has premium channels ☑ HBO? Sundance? Both? Are people renting modems?
  • In any case, more expensive bundles are (unobservably) more attractive

Instruments

  • sum of characteristics of other products produced by the firm (BLP)
  • price of similar products by the same firm in other markets (Hausman)
  • Usual concern? What identifies the price-slope? demand shifts? supply shifts?
  • Usually get positive slope demands when unobersved characteristics are correlated with price
  • Plans with more premium channels tend to be more attractive and more expensive

Instruments: examples

  • Valid: correlated with prices
  • Exogenous: uncorrelated with unovserbable utility

BLP instruments

  • Price of ETB's Plan Trío in Bogotá is instrumented with characteristics of Triple Play plans offered by Claro, UNE and Movistar.

Hausman instruments

  • Price of UNE's Silver 5 Megas in Medellín is instrumented with prices of the same plan in Rionegro, Bello, Envigado and Sabaneta.
  • good: correlated with price
  • uncorrelated with unobserved utility
  • BLP exo comes from timing
  • Hausman: after controlling for demographic market shocks are uncorrelated

Supply side

Firms are assumed to play a static Bertrand game

  • Differentiated goods: characteristics are decided before the game starts
  • Each firm decides a vector of prices after observing its rivals' poducts' characteristics
  • Under such conditions, the firms' FOC for a market are given by

(9)p=mc+Ω(p)1s(p)

  • Ω(p)=Os(p)
    • O is I×I with (m,n) element 1 if products m and n are sold by the same firm.
    • s(p) the price derivatives of the shares
  • The Bertrand-Nash equilibrium is the p that solves the system
  • Why assume Bertrand? Known FOC
  • Size of P is numbre of goods in market
  • Matrix Omega prodcut of 2

How to do counterfactuals

  • To alter market structures just change O accordingly and solve for the new equilibrium

(10)ppost=mc^+[Oposts(ppost)]1s(ppost)

  • Example:

(11)O=[100010001]Opost=[110110001]

  • Computanionally involved but now we can reap the rewards
  • Estimation is hard, but simmulation is easy
  • Example

Results

Results: product characteristics

Estimates
Bundle characteristics
OLS BLP
Instruments
Hausman
Instruments
Random Coef.
Price -0.163 ** -0.381 ** -0.411 ** -0.479 **
0.081 0.154 0.178 0.224
Download speed 2.235 ** 1.571 ** 1.792 ** 2.012 *
1.047 0.648 0.841 1.088
Premium channels -0.065 0.031 0.028 0.091 *
0.067 0.045 0.029 0.046
HD channels 0.078 * 1.318 ** 1.285 ** 1.247 *
0.069 0.661 0.517 0.661
Gb included (Mobile) 1.046 ** 1.048 ** 1.035 ** 1.037 **
0.482 0.511 0.456 0.496
Minutes included (Cell) 0.045 *** 0.029 ** 0.035 ** 0.027 **
0.012 0.013 0.012 0.012
Gb included (Cell) 0.468 *** 0.402 ** 0.395 ** 0.673 **
0.114 0.2 0.197 0.285
Minutes included (Landline) -0.035 0.018 0.018 0.017
0.231 0.086 0.077 0.45
Constant -12.489 *** -12.952 *** -13.457 *** -14.357 ***
0.315 1.47 1.057 2.714
Continuous variables in logs.
Number of osbervations: 115,545
Signif. codes: 0.001(***) 0.05(**) 0.10(*).
  • around 10% inelastic demands
  • Both isntruments reveal the existence of a downard bias
  • Instruments solve the problem of this weird negative sign on minutes included
  • logit gets the wrong substi patterns

Results: Susbtitution patterns

Estimated substitution patterns
Γ   Dollars
Internet/Phone 0.82 ***   $0.92
0.13  
Internet/TV -1.78 *   -$2.00
1.6  
Internet/Mobile 1.35 ***   $1.51
0.27  
Internet/Cell 2.79 **   $3.13
1.38  
Phone/TV 0.92 ***   $1.03
0.04  
Phone/Mobile -2.11 ***   -$2.36
0.57  
Phone/Cell 1.02 ***   $1.15
0.02  
TV/Mobile 0.02   $0.02
0.01  
TV/Cell 1.73 ***   $1.94
0.01  
Mobile/Cell 2.57 *   $2.89
1.22  
Internet/Phone/TV 1.17 ***   $1.31
0.26  
The estimation algorithm is initialized with simple Logit estimates. *** significant at 1%;** significant at 5%

All signigifacnt except for TV-Mobile

Results: Susbtitution patterns

Estimated substitution patterns
Γ   Dollars
Internet/Phone 0.82 ***   $0.92
0.13  
Internet/TV -1.78 *   -$2.00
1.6  
Internet/Mobile 1.35 ***   $1.51
0.27  
Internet/Cell 2.79 **   $3.13
1.38  
Phone/TV 0.92 ***   $1.03
0.04  
Phone/Mobile -2.11 ***   -$2.36
0.57  
Phone/Cell 1.02 ***   $1.15
0.02  
TV/Mobile 0.02   $0.02
0.01  
TV/Cell 1.73 ***   $1.94
0.01  
Mobile/Cell 2.57 *   $2.89
1.22  
Internet/Phone/TV 1.17 ***   $1.31
0.26  
The estimation algorithm is initialized with simple Logit estimates. *** significant at 1%;** significant at 5%
  • pairs higlihted show extra utility of joint consumption and their valuation
  • Beyond the point estimates the takeway is that, in general, wired and wireless are complements.

Counterfactuals

Alternative scenarios

With estimates for the preferences, we are ready to simulate 2 scenarios:

  • Merger of ETB (Wired) and Avantel (Wireless)
    • Allows to see the effect on price of standalone goods.
    • Doesn't capture bundling.
  • De-merger of Claro into Telmex (Wired) and Comcel (Wireless)
    • Captures the effect of bundling and price discrimination.
    • Helps scrutinize merger in hindsight.
  • Interest for policy makers
  • avantel is the last exclusive mobile carrier
  • claro: previous simulation doesn't capature effect of bundling

Merger ETB and Avantel

  • Slight increase price of standalone
  • reduction price of bundles(NOT from the merged!!!!)

Merger ETB and Avantel (cont.)

Prices of standalone goods
1st
Quartile
2nd
Quartile
3rd
Quartile
Mean Min. Max.
Baselinea $7.79 $10.40 $16.01 $13.68 $1.03 $49.28
Mergerb $8.23 $10.72 $16.31 $13.72 $0.95 $49.21
(a) Original market structure; (b) ETB and Avantel.
Price of bundled goods
1st
Quartile
2nd
Quartile
3rd
Quartile
Mean Min. Max.
Baselinea $14.16 $17.02 $25.88 $20.28 $8.31 $153.83
Mergerb $13.33 $14.59 $22.55 $18.49 $7.15 $153.97
(a) Original market structure; (b) ETB and Avantel.
  • The average households gets a little over one extra dollar worth of consumers surplus under the new price distribution.
  • The total gain in consumer surplus for the 13 largest cities is over 7 million.
  • 1st and 3rd quartile increase as well as mean (min and max cheaper)
  • More interesting than the price distribution is the effect on consumer surplus: 7 million dollars for 13 largest MSA

De-merger of Claro

  • Largest carrier and second largest ISP
  • Sneakiest move in the history of mergers

De-merger of Claro (cont.)

Price of standalone goods
1st
Quartile
2nd
Quartile
3rd
Quartile
Mean Min. Max.
Baselinea $7.23 $9.72 $15.30 $12.90 $0.05 $49.12
Mergerb $6.79 $9.40 $15.01 $12.86 $0.03 $49.13
(a) Original market structure; (b) ETB and Avantel.
Price of bundled goods
1st
Quartile
2nd
Quartile
3rd
Quartile
Mean Min. Max.
Baselinea $18.48 $28.60 $37.62 $28.57 $5.99 $158.18
Mergerb $20.72 $28.96 $37.45 $29.42 $6.15 $158.22
(a) Original market structure; (b) ETB and Avantel.
  • The consumer surplus is reduced by a little more than $11 million.
  • As expected there is a slight reduction in standalone prices but the price of bundles increases

  • Consumer variation is a negative 11 million

Final remarks

Extensions

  • Allow for cost synergies.
  • Endognenous product characteristics as in Draganska et al. (2009) and Fan (2013).
  • Switching costs (Lock-in)

Policy implications

  • Stronger complementarities require less stringent scrutiny
  • Allow bundling
  • Investment on complements as alternative to divestiture (horizontal mergers)

Future work

  • Rule of thumb for regulators
  • Endogenous product characteristics
  • Price discrimination
  • VNMO

Thanks!

Summary statistics: households

Summary statistics of household characteristics
Means and standard deviations
Stratum 1 Stratum 2 Stratum 3 Stratum 4 Stratum 5 Stratum 6
Avg. Schooling a 6.94 8.28 9.72 11.75 11.85 13.86
2.45 2.49 2.96 3.42 3.27 3.42
HH age b 0.68 0.61 0.62 0.59 0.56 0.65
0.47 0.49 0.49 0.49 0.5 0.48
Family size 5.34 4.76 3.65 3.86 3.59 3.37
3.45 2.27 1.48 1.62 1.53 1.52
Monthly income c 386.26 534.17 556.37 933.36 1084.72 1957.3
366.42 363.72 523.72 997.25 1549.46 2277.01
Observations 720 720 720 720 390 390
a: sum of the number of years of schooling for people in the household, devided by the number of members of the household.
b: head of the household is between 25 and 45 years old.
c: Monthly income in 2015 dollars

(go back)

Choice probabilities

Let u=(uA,uB,uAB)F(u). Then, the choice probabilities are:

(12)qA=uI(uA0)I(uAuB)I(uAuAB)dF(u)
(13)qB=uI(uB0)I(uBuA)I(uBuAB)dF(u)
(14)qAB=uI(uAB0)I(uABuA)I(uABuB)dF(u)

Let QA=qA+qAB and QB=qB+qAB.

Definition: A and B are complements if QApB<0, susbtitutes ifQApB>0 and independent if QApB=0.

Substitution patterns: pB and Γ=0


  • marginal consumers like m will substitute AB for A,
  • qA=↓qAB and QA remains unchanged.
  • marginal consumers like n have no impact on QA
  • Hence Γ=0 implies QApB=0 (goods are independent)

Substitution patterns: pB and Γ>0


  • marginal consumers like m will substitute AB for A
  • qA=↓qAB
  • marginal consumers like o will substitute AB for neither good
  • qA and overall QA
  • Hence Γ>0 implies QApB<0 (goods are complements)

Substitution patterns: pB and Γ<0


  • marginal consumers like m will substitute AB for A,
  • qA=↓qAB
  • marginal consumers like o will substitute B for A
  • qA and overall QA
  • Hence Γ<0 implies QApB>0 (goods are substitutes) (go back)

References

Anderson, Simon P., Simon Loertscher, and Yves Schneider. 2010. “The ABC of Complementary Products Mergers.” Economics Letters 106 (3): 212–15. doi:10.1016/j.econlet.2009.11.022.

Choi, Jay Pil. 2008. “Mergers with Bundling in Complementary Markets.” The Journal of Industrial Economics 56 (3). wiley: 553–77. doi:10.1111/j.1467-6451.2008.00352.x.

Flores-Fillol, Ricardo, and Rafael Moner-Colonques. 2011. “Endogenous Mergers of Complements with Mixed Bundling.” Review of Industrial Organization 39 (3): 231–51. doi:10.1007/s11151-011-9281-0.

Gentzkow, Matthew. 2007. “Valuing New Goods in a Model with Complementarity: Online Newspapers.” American Economic Review 97 (3): 713–44. doi:10.1257/aer.97.3.713.

Peitz, Martin. 2008. “Bundling May Blockade Entry.” International Journal of Industrial Organization 26 (1): 41–58. doi:10.1016/j.ijindorg.2006.09.005.

Ribeiro, Ricardo, and Joao Vareda. 2010. “Crowding Out or Complementarity in the Telecommunications Market.” Economics Letters 106 (3). Elsevier: 212–15.

Spector, David. 2007. “Bundling, Tying, and Collusion.” International Journal of Industrial Organization 25 (3): 575–81. doi:10.1016/j.ijindorg.2006.06.003.